DataEngConf Features DSI Faculty Members
Three faculty members from the Data Science Institute (DSI) were featured speakers during the DataEngConf NYC’ 17, a conference aimed to bridge the gap between data science, data engineering and data analytics.
The conference, held Oct. 30 in Columbia University’s Lerner Hall, featured top data science teams from Google, the New York Times, Stanford University, Dremio, Citus Data, Buzzfeed and the Data Science Institute. Hosted by Hakka Labs and co-sponsored by the DSI, more than fifty data scientists and engineers discussed the architectures of data pipelines and platforms as well applied and practical examples of data science.
Adam Kelleher, an Adjunct Assistant Professor with DSI and principal data scientist at Buzzfeed, moderated a panel discussion on data science and the media. Featured on the panel was Chris Wiggins, who serves on DSI’s Executive Committee and is the Chief Data Scientist for the New York Times. Wiggins and other panelists discussed developments in machine learning, artificial intelligence and related issues. The panelists also discussed changes in European data regulation, the growth of artificial intelligence and innovations in cloud technologies and machine-learning services.
Andreas Mueller, a lecturer at DSI and author of "Introduction to Machine Learning with Python” summarised developments in automatic machine learning and offered an overview of the best available tools for it. Mueller, a core developer of the scikit-learn machine-learning library, also discussed how to use python and scikit-learn to devise a practical approach to machine learning.
"Once you understand and select the right parameters and search methods, you'll see how helpful machline learning can be in all domains," said Mueller. "Most data scientists spend much more time collecting their data and less time on selecting the best models to analyze that data. I want to make machine learning acccesible to help everyone better analyze their data."
--By Haniya Javed